Conventionally, signal data obtained from signals such as electrical signals is stored after sampling the signal and digitizing the samples of the signal. A signal is a time/space position dependent phenomenon. When the signal is a temporal signal, the signal may be sampled at discrete time points. When the signal is a spatial signal, the signal may be sampled at discrete spatial points. The stored signal data may subsequently be used for processing, for example by applying filter operations, interpolation, extrapolation etc. Often only signal data for a limited window of time or space positions is kept in store for this purpose. Storage space requirements rise with the size of the window, and they can become a significant burden if no severe limitation is imposed on the window size.
U.S. Pat. No. 7,236,971 discloses a database system with a data base of signal data. The system is capable of performing interpolation (e.g., temporal interpolation) of signal data from the data base in response to receiving a database query. In one implementation, the database query contains an interpolation function. This system makes it possible to deliver different versions of the data, interpolated in different ways, based on the stored signal data.
Caching of interpolated signal data is described in an article by Deshpande et al, titled “MauvDB”, published at the ACM Sigmod International conference on management of data June 2006, Chicago, page 73-84 (EPO reference XP55020827). Deshpande et al describe a database system that supports queries that define results consisting of processed data, such as interpolated and fitted data, derived from raw data in the database, or a result of other queries. Deshpande et al mention an efficiency problem due to the fact that every time that a new value is added to the database a complete rescan of all the data may be needed. To solve this problem, Deshpande et al mention the possibility of caching earlier results, so that only values associated with the newly added data need to be computed. Deshpande et al also disclose that the queries could define different operation for different data partitions, for example different regression functions to be used for different time steps.
The database could be used to store the original signal data of a signal as well as signal data obtained by processing the signal, so that repeated processing can be avoided when the processed data is needed repeatedly. However, this may add to the burden of storage space requirements.
Semantic caching, i.e. caching intermediate results in combination with information how the results have derived is known from articles by Andrade et al and Dar et al. The article by Andrade et al is titled “Optimizing the execution of multiple data analysis queries on parallel and distributed environments, published in the IEEE Transactions on parallel and distributed systems Vol 15 15 No 6 pages 520-532 (EPO reference XP011111874). The article by Dar et al is titled ““Semantic data caching and replacement” published in the Proceedings of the International Conference on very large databases 1996 pages 1-12 (EPO reference XP002261689).